In general, such device and method are used for the exchange of electronic data between two or more devices by wireless data communication. The communication devices may be part of a data communication network.
Among such data networks are so-called user or person localization systems which may be used, for example, in human health management. For example, dementia patients living in nursery homes often get lost on walks outside of their common surroundings if unattended. Therefore it is critical for nurses to be notified if their proteges intend to take a walk. A system supporting patients in critical situations transmitting their current location to the nurses is therefore needed.
A system referred to as KopAL system (Fudickar et al., “A Mobile Orientation System For Dementia Patients,” in Proc. of International Conference on Intelligence Interactive Assistance and Mobile Multimedia Computing, Rostock, Germany, November 2009; Fudickar et. al., “KopAL—An Orientation System For Patients With Dementia,” in Behaviour Monitoring and Interpretation—BMI. IOS Press, 2011, pp. 83-104) supports elderly suffering from potential dementia. They are equipped with mobile devices that autonomously take care of them by reminding them of upcoming appointments, recognizing critical situations (in case of falling or losing-tracks) and offering emergency-call functionality.
For localization system the use of different of technologies such as GPS-like satellite systems, GSM-like cellular radios, Wi-Fi, Bluetooth, RFID, and DECT was proposed. The location is predicted via received radio waves, e.g. by triangulation. The location prediction accuracy thereby depends mainly on the range, the measurements accuracy (next to the availability of reference nodes, their geometry and location information).
Radio signal quality is influenced when transmitted through materials such as water, metal or stone. In the frequency ranges of GPS and GSM these influences are problematic, making it hard to estimate a precise distance in buildings (indoor) (see Kaplan et al., Understanding GPS: principles and applications, 2nd ed. Artech House, 2005; Varshaysky et al., “Gsm indoor localization,” Pervasive Mob. Comput., vol. 3, pp. 698-720, December 2007). As a result, outdoor localization techniques are not appropriate for indoor scenarios.
Several alternative techniques have been proposed for indoor localization, with deviation in precision of only a few centimeters. In systems using RFID (Ni et al., “Landmarc: indoor location sensing using active rfid,” Wirel. Netw., vol. 10, pp. 701-710, November 2004; Cox et al., “Intellibadge: Towards providing location-aware value-added services at academic conferences,” UbiComp 2003 Ubiquitous Computing, vol. 2864, pp. 264-280, 2003) or passive infrared (PIR) users are equipped with passive badges, which are identified by stationary receivers. The short transmission range of infrared (since requiring line of sight) assures a precise location, but requires a high density of receivers. Localization systems using RFID badges in contrast have limited precision caused by environmental influences on radio signal strength and low detection precision.
These approaches use artificial enriched environments which include increased deployment costs and privacy concerns and thereby have limited applicability.
An alternative to these artificial enriched environments are systems that compute the location on user devices, requiring little or no additional equipment in the environment. Therefore Bluetooth, Wi-Fi, Ultrasound, UWB, and even DECT are more suitable, since their algorithms can be executed within user attached devices (see Kyamakya et al., “An indoor Bluetoothbased positioning system: concept, implementation and experimental evaluation,” International, 2003; Bahl et al., “RADAR: an in-building RF based user location and tracking system,” INFOCOM 2000. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE, vol. 2, pp. 775-784 vol. 2, 2000; Hightower et al., “Practical lessons from place lab,” IEEE Pervasive Computing, vol. 5, pp. 32-39, July 2006; Smith et al., “Tracking Moving Devices with the Cricket Location System,” in 2nd International Conference on Mobile Systems, Applications and Services (Mobisys 2004), Boston, Mass., June 2004; Gezici et al., “Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks,” Signal Processing Magazine, IEEE, vol. 22, no. 4, pp. 70-84, 2005; M. Kranz et al., “A comparative study of DECT and WLAN signals for indoor localization,” in 8th Annual IEEE Int'l Conference on Pervasive Computing and Communications (PerCom 2010). Mannheim, Germany: IEEE, March 2010, pp. 235-243)
The utilization of these localization techniques is challenging for several reasons. The distance between devices can be computed based on transmitted radio signals via either the angle of arrival (AOA), time based approaches (such as TOA, TDOA) or the received signal strength indicator (RSSI). The RSSI based distance calculation is the most practical for mobile devices, in common sense (Savarese et al., “Locationing in distributed ad-hoc wireless sensor networks,” in in ICASSP, 2001, pp. 2037-2040). However, the radio signal strength is highly influenced by obstacles in the line of sight such as concrete walls, metal, humans or plants (Cheng et al., “Accuracy characterization for metropolitan-scale wi-fi localization,” in Proceedings of the 3rd international conference on Mobile systems, applications, and services, ser. MobiSys '05. New York, N.Y., USA: ACM, 2005, pp. 233-245) causing reflections, refractions, diffractions, absorptions, polarizations and scattering of the radio-waves (J. D. Parsons, The Mobile Radio Propagation Channel, 2nd Edition, 2nd ed. Wiley, November 2000).
These effects are very critical within the spectrum of 2.4 GHz as utilized by Wi-Fi and Bluetooth (Hossain et al., “A Comprehensive Study of Bluetooth Signal Parameters for Localization,” in Proc. IEEE PIMRC, Athens, Greece, September 2007; Zhou et al., “Position measurement using Bluetooth,” IEEE Transactions on Consumer, 2006) and UWB (Cho et al., Performance Tests for Wireless Real-time Localization Systems to Improve Mobile Robot Navigation in Various Indoor Environments, 2008). Here the signal strength does not decrease steadily even in a line of sight (Elnahrawy et al., “The limits of localization using signal strength: a comparative study,” 2004, pp. 406-414; Chandrasekaran et al., “Empirical evaluation of the limits on localization using signal strength,” in Proceedings of the 6th Annual IEEE communications society conference on Sensor, Mesh and Ad Hoc Communications and Networks, ser. SECON '09. Piscataway, N.J., USA: IEEE Press, 2009, pp. 333-341; Otsason et al., “Accurate gsm indoor localization,” in UbiComp 2005: Ubiquitous Computing, ser. Lecture Notes in Computer Science, M. Beigl, S. Intille, J. Rekimoto, and H. Tokuda, Eds. Springer Berlin/Heidelberg, 2005, vol. 3660, pp. 903-903).
As a result, the received signal strength must be interpreted in the context of the surroundings, requiring initial system calibration via multiple RSS sampling at calibration points during the so called off-line phase—similar to Wi-Fi or GSM fingerprints such as utilized by RADAR, PlaceLab or TV-GPS (“Rosum tv-gps,” Website, 2011, available online at http://www.trueposition.com/visited on Sep. 15, 2011)). By inclusion of probabilistic methods such as particle filter, kayman filter or hidden marcov model the impact of imprecise radio strength measurements is reduced resulting in increased location accuracy.
However, the requirements for localization algorithms can not focus only on prediction accuracy. Instead, memory usage, time performance (Hightower et al., “Particle filters for location estimation in ubiquitous computing: A case study,” in Proceedings of the Sixth International Conference on Ubiquitous Computing (Ubicomp 2004), ser. Lecture Notes in Computer Science, N. Davies, E. Mynatt, and I. Siio, Eds., vol. 3205. Springer-Verlag, September 2004, pp. 88-106) as well as energy consumption are most important when executed on mobile devices. Energy consumption was considered as a criteria, suggesting as a result the combination of sub 1 GHz and ultra sound for precision up to centimeters (see Balakrishnan et al., “Lessons from developing and deploying the cricket indoor location system,” Tech. Rep., 2003).
Rice et al. analyzed wireless communication on android smart phones regarding the influence of message size and send buffer was analyzed. The idle power consumption of Wi-Fi, 3G and 2G communication networks was compared. It was found that the base power consumption of Wi-Fi was the lowest, while 2G had the highest consumption.
Most users recharge their devices context-dependent e.g. while sleeping. Only 28% of an interviewgroup recharge their devices with explicit knowledge of the current battery load. Our interviews with nurses and the elderly in home-stay had similar results, indicating the user's preference to recharge their mobile device over night. As a result, energy consumption is critical if falling below a periodic runtime of one day.
Document EP 2 169 924 A1 relates to a mobile wireless communications device that is configured to operate over five frequency bands, a 850 MHz global system for mobile communications (GSM) band, a 900 MHz GSM band, a DCS band, a PCS band and a WCDMA band (up to about 2100 MHz). Further, the device comprises a Near Field Communications circuit.
In document WO 2007/144014 A1 a mobile phone is disclosed that is operable for a number of frequency bands, including the GSM 900/1800 MHz network.
According to the GSM specification different frequency ranges that do not overlap with each other are used for sending data from a mobile device to a base station than for receiving data by the mobile device. For example, in the GSM 850 band data is sent from the mobile device to the base station in the frequency range from 824 to 849 MHz. But, data is received by the mobile device in the frequencies ranging from 869 to 894 MHz.